Internet radio is an audio service to distribute audio content via the Internet to individual consumers. Today, music is the most popular type of audio content that is distributed. However, news, weather information, audio books, spoken commentaries, e-learning content, or the like are also offered on the Internet to an audience or a community of listeners. This is sometimes referred to as webcasting since it is not transmitted broadly and directly to its listeners through wireless means only.
Similar to traditional radio broadcasting, Internet radio involves streaming media, which is presenting listeners with a continuous stream of audio that cannot be paused or replayed by a listener. In this respect, it is distinct from on-demand file serving. Internet radio is also distinct from podcasting individual content, which involves downloading rather than streaming. Many Internet radio services are associated with a corresponding traditional (terrestrial) radio station or radio network. Internet-only radio stations are independent of such associations.
While Internet radio services are usually accessible from anywhere in the world, some stations and networks restrict listening to within the country. Internet radio services offer news, sports, talk, and various genres of music, such as Blues, Classical, Country, Easy Listening, Electronic, Folk, Jazz, Latin, Metal, Pop, R&B and Urban, Rap, Reggae, Rock, or others, in a similar manner as they are available on traditional radio stations.
Streaming technology is used to distribute audio content that is encoded. Streaming audio coding formats include MP3, Ogg Vorbis, Windows Media Audio, RealAudio, or others. Audio data is continuously transmitted serially (streamed) over the local network or Internet in TCP or UDP packets, then reassembled at the receiver and presented to the listener. Most stations stream their content at data rates between 64 kbit/second and 128 kbit/second, providing near CD quality audio.
Currently known Internet radio stations such as Pandora, geographically restricted to the U.S., rely solely on an analysis of music by musician-analysts that have been listening to music song by song in order to analyze and collect musical details on every track such as melody, harmony, instrumentation, rhythm, vocals, lyrics etc. Based on, e.g., a name of a song, artist or genre entered by a user, the database is scanned to include tracks with musical similarities in a playlist. The tracks of that playlist will then be streamed to the listener's Internet device (desktop, laptop, or tablet computer, smartphone, e-book with Internet access, or the like).
Growing media libraries are more and more difficult to effectively manage. Music playlists are one way to effectively manage and filter certain songs by selecting groupings of songs for ordered playback. While handcrafting a playlist typically involves the tedious process of searching through a list of media to find appropriate songs and selecting the desired songs, music playlists can also be automatically generated based on common music track attributes, such as genre, artist, album, and the like. These automatically generated playlists, while simple and fast to create, usually result in playlists with low acceptance by listeners. Further, such automatic methods assume that all relevant track attributes are available and accurate for each piece of media. One way of generating automatic playlists involves a user to specify search criteria, adds songs matching the search criteria to the playlist, and automatically updates the playlist as songs meet the criteria. However, even these playlists are limited by a user's musical familiarity, library, and skill in crafting an effective playlist.
A main drawback of conventional media playlist generation is determining which media items are similar to one another by one or more aspects. When handcrafting a playlist the user is responsible for drawing similarities between several different media items. Media similarity data and, more specifically, media playlists based on media similarity data are known in the art. Systems using similarity data for this purpose identify characteristics within each track and identify those tracks as being similar. Automated playlist generators rely on criteria such as melody, harmony, instrumentation, rhythm, vocals, lyrics etc. to build playlists, but these criteria are often too broad. Media by the same artist or even of the same type or genre is often not similar enough to generate a desirable playlist, i.e., where the listener's preferences are adequately taken into account. Some playlists try to solve some of these problems based on more detailed characteristics of the media, but they do not account for human preferences that are not easily definable.
As media libraries grow and digital media players are available with ever increasing capacities, these problems with playlists are likely to be exacerbated. Accordingly, what is needed in the art is an improved method of generating similarity data between media files and using such data for creating and managing playlists. However, in the rapidly growing number of Internet radio stations, currently known solutions are found to be inadequate to generate sufficiently attractive playlists of content. Therefore, the reach of each Internet radio station can be quite limited in the future due to its lack of focus on its listener community.